30,489 research outputs found

    Cost reduction using process analysis in company PEGRES obuv s.r.o.

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    Firma PEGRES obuv s.r.o. se již delší dobu potýká se stagnací v oblasti plánování a řízení výroby. Některé podnikové procesy jsou nyní značně zastaralé a v aktuálních podmínkách již neefektivní. Cíl práce je snížení nákladů s využitím procesní analýzy. Pro dosažení tohoto cíle bude provedena analýza současného stavu zastaralých procesů a budou popsány vybrané metody řízení výroby, které jsou svou povahou relevantní pro výrobu obuvi. Výstupem práce je sada doporučení a návrhů na změny v existujících procesech. Vybrané návrhy budou v prostředí firmy implementovány a práce zahrne zhodnocení výsledků po zavedení těchto změn.Company PEGRES obuv s.r.o. has been long time struggling with stagnation in production planning and control. Some of the internal processes are now obsolete and in current conditions no longer effective. The goal of the paper is to reduce the costs using process analysis. To achieve this goal, analysis of the current state of outdated processes will be performed, followed by description of selected methods of production management, which by their nature are relevant to the production of the shoes. Output of the work is a set of recommendations and proposals for changes to existing processes. Selected proposals will be implemented in the company and paper will include evaluation of results after the implementation of these changes.

    A quality management based on the Quality Model life cycle

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    Managing quality is a hard and expensive task that involves the execution and control of processes and techniques. For a good quality management, it is important to know the current state and the objective to be achieved. It is essential to take into account with a Quality Model that specifies the purposes of managing quality. QuEF (Quality Evaluation Framework) is a framework to manage quality in MDWE (Model-driven Web Engineering). This paper suggests managing quality but pointing out the Quality Model life cycle. The purpose is to converge toward a quality continuous improvement by means of reducing effort and time.Ministerio de Ciencia e Innovación TIN2010-20057-C03-02Ministerio de Ciencia e Innovación TIN 2010-12312-EJunta de Andalucía TIC-578

    Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches

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    Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements

    Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

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    This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations
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